{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading facial landmark predictor...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\mydownload\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:152: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "嘴巴实时长宽比:0.52 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.21 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.54 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
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      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.54 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.54 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
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      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.52 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.52 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.54 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.52 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
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      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.54 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.53 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.50 \t是否张嘴：True\n",
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      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
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      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
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      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.51 \t是否张嘴：True\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
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      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
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      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
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      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
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      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
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      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.42 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.41 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# import the necessary packages\n",
    "from scipy.spatial import distance as dist\n",
    "from imutils.video import FileVideoStream\n",
    "from imutils.video import VideoStream\n",
    "from imutils import face_utils\n",
    "import numpy as np # 数据处理的库 numpy\n",
    "import argparse\n",
    "import imutils\n",
    "import time\n",
    "import dlib\n",
    "import cv2\n",
    " \n",
    " \n",
    "def eye_aspect_ratio(eye):\n",
    "    # 垂直眼标志（X，Y）坐标\n",
    "    A = dist.euclidean(eye[1], eye[5])# 计算两个集合之间的欧式距离\n",
    "    B = dist.euclidean(eye[2], eye[4])\n",
    "    # 计算水平之间的欧几里得距离\n",
    "    # 水平眼标志（X，Y）坐标\n",
    "    C = dist.euclidean(eye[0], eye[3])\n",
    "    # 眼睛长宽比的计算\n",
    "    ear = (A + B) / (2.0 * C)\n",
    "    # 返回眼睛的长宽比\n",
    "    return ear\n",
    " \n",
    "def mouth_aspect_ratio(mouth):\n",
    "    A = np.linalg.norm(mouth[2] - mouth[9])  # 51, 59\n",
    "    B = np.linalg.norm(mouth[4] - mouth[7])  # 53, 57\n",
    "    C = np.linalg.norm(mouth[0] - mouth[6])  # 49, 55\n",
    "    mar = (A + B) / (2.0 * C)\n",
    "    return mar\n",
    "\n",
    "\n",
    "# 定义两个常数\n",
    "# 眼睛长宽比\n",
    "# 闪烁阈值\n",
    "EYE_AR_THRESH = 0.2\n",
    "EYE_AR_CONSEC_FRAMES = 3\n",
    "# 打哈欠长宽比\n",
    "# 闪烁阈值\n",
    "MAR_THRESH = 0.5\n",
    "MOUTH_AR_CONSEC_FRAMES = 3\n",
    "# 初始化帧计数器和眨眼总数\n",
    "COUNTER = 0\n",
    "TOTAL = 0\n",
    "# 初始化帧计数器和打哈欠总数\n",
    "mCOUNTER = 0\n",
    "mTOTAL = 0\n",
    " \n",
    "# 初始化DLIB的人脸检测器（HOG），然后创建面部标志物预测\n",
    "print(\"[INFO] loading facial landmark predictor...\")\n",
    "# 第一步：使用dlib.get_frontal_face_detector() 获得脸部位置检测器\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "# 第二步：使用dlib.shape_predictor获得脸部特征位置检测器\n",
    "predictor = dlib.shape_predictor('D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat')\n",
    " \n",
    "# 第三步：分别获取左右眼面部标志的索引\n",
    "(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"left_eye\"]\n",
    "(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"right_eye\"]\n",
    "(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"mouth\"]\n",
    "\n",
    "# 第四步：打开cv2 本地摄像头\n",
    "cap = cv2.VideoCapture(0)\n",
    " \n",
    "# 从视频流循环帧\n",
    "while True:\n",
    "    # 第五步：进行循环，读取图片，并对图片做维度扩大，并进灰度化\n",
    "    ret, frame = cap.read()\n",
    "    frame = imutils.resize(frame, width=720)\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    # 第六步：使用detector(gray, 0) 进行脸部位置检测\n",
    "    rects = detector(gray, 0)\n",
    "    \n",
    "    # 第七步：循环脸部位置信息，使用predictor(gray, rect)获得脸部特征位置的信息\n",
    "    for rect in rects:\n",
    "        shape = predictor(gray, rect)\n",
    "        \n",
    "        # 第八步：将脸部特征信息转换为数组array的格式\n",
    "        shape = face_utils.shape_to_np(shape)\n",
    "        \n",
    "        # 第九步：提取左眼和右眼坐标\n",
    "        leftEye = shape[lStart:lEnd]\n",
    "        rightEye = shape[rStart:rEnd]\n",
    "        # 嘴巴坐标\n",
    "        mouth = shape[mStart:mEnd]\n",
    "        \n",
    "        \n",
    "        # 第十步：构造函数计算左右眼的EAR值，使用平均值作为最终的EAR\n",
    "        leftEAR = eye_aspect_ratio(leftEye)\n",
    "        rightEAR = eye_aspect_ratio(rightEye)\n",
    "        ear = (leftEAR + rightEAR) / 2.0\n",
    "        # 打哈欠\n",
    "        mar = mouth_aspect_ratio(mouth)\n",
    " \n",
    "        # 第十一步：使用cv2.convexHull获得凸包位置，使用drawContours画出轮廓位置进行画图操作\n",
    "        leftEyeHull = cv2.convexHull(leftEye)\n",
    "        rightEyeHull = cv2.convexHull(rightEye)\n",
    "        cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\n",
    "        cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\n",
    "        mouthHull = cv2.convexHull(mouth)\n",
    "        cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1)\n",
    " \n",
    "        # 第十二步：进行画图操作，用矩形框标注人脸\n",
    "        left = rect.left()\n",
    "        top = rect.top()\n",
    "        right = rect.right()\n",
    "        bottom = rect.bottom()\n",
    "        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3)    \n",
    " \n",
    "        '''\n",
    "            分别计算左眼和右眼的评分求平均作为最终的评分，如果小于阈值，则加1，如果连续3次都小于阈值，则表示进行了一次眨眼活动\n",
    "        '''\n",
    "        # 第十三步：循环，满足条件的，眨眼次数+1\n",
    "        if ear < EYE_AR_THRESH:# 眼睛长宽比：0.2\n",
    "            COUNTER += 1\n",
    "           \n",
    "        else:\n",
    "            # 如果连续3次都小于阈值，则表示进行了一次眨眼活动\n",
    "            if COUNTER >= EYE_AR_CONSEC_FRAMES:# 阈值：3\n",
    "                TOTAL += 1\n",
    "            # 重置眼帧计数器\n",
    "            COUNTER = 0\n",
    "            \n",
    "        # 第十四步：进行画图操作，同时使用cv2.putText将眨眼次数进行显示\n",
    "        cv2.putText(frame, \"Faces: {}\".format(len(rects)), (10, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.putText(frame, \"Blinks: {}\".format(TOTAL), (150, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.putText(frame, \"COUNTER: {}\".format(COUNTER), (300, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) \n",
    "        cv2.putText(frame, \"EAR: {:.2f}\".format(ear), (450, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        \n",
    "        '''\n",
    "            计算张嘴评分，如果小于阈值，则加1，如果连续3次都小于阈值，则表示打了一次哈欠，同一次哈欠大约在3帧\n",
    "        '''\n",
    "        # 同理，判断是否打哈欠    \n",
    "        if mar > MAR_THRESH:# 张嘴阈值0.5\n",
    "            mCOUNTER += 1\n",
    "            cv2.putText(frame, \"Yawning!\", (10, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        else:\n",
    "            # 如果连续3次都小于阈值，则表示打了一次哈欠\n",
    "            if mCOUNTER >= MOUTH_AR_CONSEC_FRAMES:# 阈值：3\n",
    "                mTOTAL += 1\n",
    "            # 重置嘴帧计数器\n",
    "            mCOUNTER = 0\n",
    "        cv2.putText(frame, \"Yawning: {}\".format(mTOTAL), (150, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.putText(frame, \"mCOUNTER: {}\".format(mCOUNTER), (300, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) \n",
    "        cv2.putText(frame, \"MAR: {:.2f}\".format(mar), (480, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "            \n",
    "        # 第十五步：进行画图操作，68个特征点标识\n",
    "        for (x, y) in shape:\n",
    "            cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)\n",
    "\n",
    "    print('嘴巴实时长宽比:{:.2f} '.format(mar)+\"\\t是否张嘴：\"+str([False,True][mar > MAR_THRESH]))\n",
    "    print('眼睛实时长宽比:{:.2f} '.format(ear)+\"\\t是否眨眼：\"+str([False,True][COUNTER>=1]))\n",
    "    \n",
    "    # 确定疲劳提示\n",
    "    if TOTAL >= 50 or mTOTAL>=15:\n",
    "        cv2.putText(frame, \"SLEEP!!!\", (100, 200),cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3)\n",
    "        \n",
    "    # 按q退出\n",
    "    cv2.putText(frame, \"Press 'q': Quit\", (20, 500),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2)\n",
    "    # 窗口显示 show with opencv\n",
    "    cv2.imshow(\"Frame\", frame)\n",
    "    \n",
    "    # if the `q` key was pressed, break from the loop\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "        \n",
    "# 释放摄像头 release camera\n",
    "cap.release()\n",
    "# do a bit of cleanup\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
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      "Rotation Vector:\n",
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      " [-0.36173897]]\n",
      "Translation Vector:\n",
      " [[ -602.67591725]\n",
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      " [-3455.27427644]]\n",
      "used_time:0.046 sec\n",
      "t0:0.2258000477602581, t1:-0.5545729727352859\n",
      "pitch:2.7549289627299984, yaw:-0.9288200196860722, roll:-3.026503287015016\n",
      "Y:157, X:-53, Z:-173\n",
      "Camera Matrix :[[640.   0. 320.]\n",
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      "Rotation Vector:\n",
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      "used_time:0.046 sec\n",
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      "pitch:2.799477270398284, yaw:-0.5823957191427476, roll:0.07312405519336337\n",
      "Y:160, X:-33, Z:4\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
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      "pitch:2.7852004569727464, yaw:-0.6027156840884625, roll:0.1563480120814033\n",
      "Y:159, X:-34, Z:8\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
      "ERROR: found no face\n",
      "get_image_points failed\n",
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      " [-0.61512921]]\n",
      "Translation Vector:\n",
      " [[  54.94204302]\n",
      " [ 121.04267843]\n",
      " [3705.96803019]]\n",
      "used_time:0.047 sec\n",
      "t0:0.19289584583978514, t1:-0.910671611480024\n",
      "pitch:2.9328607190334073, yaw:-0.3739908107253364, roll:-0.05079354899126238\n",
      "Y:168, X:-21, Z:-2\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.27485337]\n",
      " [ 0.13443582]\n",
      " [-0.61127146]]\n",
      "Translation Vector:\n",
      " [[  66.359415  ]\n",
      " [ 119.54114066]\n",
      " [3672.6633849 ]]\n",
      "used_time:0.046 sec\n",
      "t0:0.17329538240261158, t1:-0.9149183128754046\n",
      "pitch:2.9543994681148003, yaw:-0.3731443296438516, roll:-0.04661997795365144\n",
      "Y:169, X:-21, Z:-2\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.26961837]\n",
      " [ 0.12381674]\n",
      " [-0.63879517]]\n",
      "Translation Vector:\n",
      " [[  75.02552673]\n",
      " [ 117.42315739]\n",
      " [3667.90711473]]\n",
      "used_time:0.051 sec\n",
      "t0:0.17318779771887322, t1:-0.9088399824579712\n",
      "pitch:2.953291069866344, yaw:-0.3892495749019018, roll:-0.03847778580438667\n",
      "Y:169, X:-22, Z:-2\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.27209216]\n",
      " [ 0.13303398]\n",
      " [-0.65876037]]\n",
      "Translation Vector:\n",
      " [[  83.38870291]\n",
      " [ 122.49154672]\n",
      " [3667.03431758]]\n",
      "used_time:0.046 sec\n",
      "t0:0.1779052660471339, t1:-0.903285763576827\n",
      "pitch:2.947128157803967, yaw:-0.4010832602822173, roll:-0.04162013995872509\n",
      "Y:168, X:-22, Z:-2\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.26814435]\n",
      " [ 0.11049873]\n",
      " [-0.66022859]]\n",
      "Translation Vector:\n",
      " [[  91.96981102]\n",
      " [ 117.52923671]\n",
      " [3653.23195557]]\n",
      "used_time:0.047 sec\n",
      "t0:0.17626599714766608, t1:-0.9035635917748506\n",
      "pitch:2.9489335748041188, yaw:-0.4011925715686553, roll:-0.028305835116103763\n",
      "Y:168, X:-22, Z:-1\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.27351666]\n",
      " [ 0.10261169]\n",
      " [-0.64716852]]\n",
      "Translation Vector:\n",
      " [[  98.48405215]\n",
      " [ 112.17462276]\n",
      " [3665.78480948]]\n",
      "used_time:0.045 sec\n",
      "t0:0.1799851628092437, t1:-0.9062535062924404\n",
      "pitch:2.945540194790092, yaw:-0.39250574125198073, roll:-0.023573597409025638\n",
      "Y:168, X:-22, Z:-1\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.27870899]\n",
      " [ 0.10103617]\n",
      " [-0.64323506]]\n",
      "Translation Vector:\n",
      " [[ 102.07915284]\n",
      " [ 119.50815867]\n",
      " [3664.13492801]]\n",
      "used_time:0.063 sec\n",
      "t0:0.1844488869990829, t1:-0.906528287506611\n",
      "pitch:2.9408653599261836, yaw:-0.38949046768526563, roll:-0.021891449898725407\n",
      "Y:168, X:-22, Z:-1\n",
      "Camera Matrix :[[640.   0. 320.]\n",
      " [  0. 640. 240.]\n",
      " [  0.   0.   1.]]\n",
      "Rotation Vector:\n",
      " [[-3.27870899]\n",
      " [ 0.10103617]\n",
      " [-0.64323506]]\n",
      "Translation Vector:\n",
      " [[ 102.07915284]\n",
      " [ 119.50815867]\n",
      " [3664.13492801]]\n",
      "used_time:0.045 sec\n",
      "t0:0.1844488869990829, t1:-0.906528287506611\n",
      "pitch:2.9408653599261836, yaw:-0.38949046768526563, roll:-0.021891449898725407\n",
      "Y:168, X:-22, Z:-1\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    " \n",
    "import cv2\n",
    "import numpy as np\n",
    "import dlib\n",
    "import time\n",
    "import math\n",
    "\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "predictor = dlib.shape_predictor(\"D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat\")\n",
    "POINTS_NUM_LANDMARK = 68\n",
    "\n",
    "# 获取最大的人脸\n",
    "def _largest_face(dets):\n",
    "    if len(dets) == 1:\n",
    "        return 0\n",
    "\n",
    "    face_areas = [ (det.right()-det.left())*(det.bottom()-det.top()) for det in dets]\n",
    "\n",
    "    largest_area = face_areas[0]\n",
    "    largest_index = 0\n",
    "    for index in range(1, len(dets)):\n",
    "        if face_areas[index] > largest_area :\n",
    "            largest_index = index\n",
    "            largest_area = face_areas[index]\n",
    "\n",
    "    print(\"largest_face index is {} in {} faces\".format(largest_index, len(dets)))\n",
    "\n",
    "    return largest_index\n",
    "\n",
    "# 从dlib的检测结果抽取姿态估计需要的点坐标\n",
    "def get_image_points_from_landmark_shape(landmark_shape):\n",
    "    if landmark_shape.num_parts != POINTS_NUM_LANDMARK:\n",
    "        print(\"ERROR:landmark_shape.num_parts-{}\".format(landmark_shape.num_parts))\n",
    "        return -1, None\n",
    "    \n",
    "    #2D image points. If you change the image, you need to change vector\n",
    "    image_points = np.array([\n",
    "                                (landmark_shape.part(30).x, landmark_shape.part(30).y),     # Nose tip\n",
    "                                (landmark_shape.part(8).x, landmark_shape.part(8).y),     # Chin\n",
    "                                (landmark_shape.part(36).x, landmark_shape.part(36).y),     # Left eye left corner\n",
    "                                (landmark_shape.part(45).x, landmark_shape.part(45).y),     # Right eye right corne\n",
    "                                (landmark_shape.part(48).x, landmark_shape.part(48).y),     # Left Mouth corner\n",
    "                                (landmark_shape.part(54).x, landmark_shape.part(54).y)      # Right mouth corner\n",
    "                            ], dtype=\"double\")\n",
    "\n",
    "    return 0, image_points\n",
    "    \n",
    "# 用dlib检测关键点，返回姿态估计需要的几个点坐标\n",
    "def get_image_points(img):\n",
    "                            \n",
    "    #gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )  # 图片调整为灰色\n",
    "    dets = detector( img, 0 )\n",
    "\n",
    "    if 0 == len( dets ):\n",
    "        print( \"ERROR: found no face\" )\n",
    "        return -1, None\n",
    "    largest_index = _largest_face(dets)\n",
    "    face_rectangle = dets[largest_index]\n",
    "\n",
    "    landmark_shape = predictor(img, face_rectangle)\n",
    "\n",
    "    return get_image_points_from_landmark_shape(landmark_shape)\n",
    "\n",
    "\n",
    "# 获取旋转向量和平移向量                        \n",
    "def get_pose_estimation(img_size, image_points ):\n",
    "    # 参考：https://www.learnopencv.com/head-pose-estimation-using-opencv-and-dlib/\n",
    "    # 3D model points.\n",
    "    model_points = np.array([\n",
    "                                (0.0, 0.0, 0.0),             # Nose tip\n",
    "                                (0.0, -330.0, -65.0),        # Chin\n",
    "                                (-225.0, 170.0, -135.0),     # Left eye left corner\n",
    "                                (225.0, 170.0, -135.0),      # Right eye right corne\n",
    "                                (-150.0, -150.0, -125.0),    # Left Mouth corner\n",
    "                                (150.0, -150.0, -125.0)      # Right mouth corner\n",
    "                             \n",
    "                            ])\n",
    "     \n",
    "    # Camera internals\n",
    "     \n",
    "    focal_length = img_size[1]\n",
    "    center = (img_size[1]/2, img_size[0]/2)\n",
    "    camera_matrix = np.array(\n",
    "                             [[focal_length, 0, center[0]],\n",
    "                             [0, focal_length, center[1]],\n",
    "                             [0, 0, 1]], dtype = \"double\"\n",
    "                             )\n",
    "     \n",
    "    print(\"Camera Matrix :{}\".format(camera_matrix))\n",
    "     \n",
    "    dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion\n",
    "    (success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE )\n",
    " \n",
    "    print(\"Rotation Vector:\\n {}\".format(rotation_vector))\n",
    "    print(\"Translation Vector:\\n {}\".format(translation_vector))\n",
    "    return success, rotation_vector, translation_vector, camera_matrix, dist_coeffs\n",
    "\n",
    "# 从旋转向量转换为欧拉角\n",
    "def get_euler_angle(rotation_vector):\n",
    "    # calculate rotation angles\n",
    "    theta = cv2.norm(rotation_vector, cv2.NORM_L2)\n",
    "    \n",
    "    # transformed to quaterniond\n",
    "    w = math.cos(theta / 2)\n",
    "    x = math.sin(theta / 2)*rotation_vector[0][0] / theta\n",
    "    y = math.sin(theta / 2)*rotation_vector[1][0] / theta\n",
    "    z = math.sin(theta / 2)*rotation_vector[2][0] / theta\n",
    "    \n",
    "    ysqr = y * y\n",
    "    # pitch (x-axis rotation)\n",
    "    t0 = 2.0 * (w * x + y * z)\n",
    "    t1 = 1.0 - 2.0 * (x * x + ysqr)\n",
    "    print('t0:{}, t1:{}'.format(t0, t1))\n",
    "    pitch = math.atan2(t0, t1)\n",
    "    \n",
    "    # yaw (y-axis rotation)\n",
    "    t2 = 2.0 * (w * y - z * x)\n",
    "    if t2 > 1.0:\n",
    "        t2 = 1.0\n",
    "    if t2 < -1.0:\n",
    "        t2 = -1.0\n",
    "    yaw = math.asin(t2)\n",
    "    \n",
    "    # roll (z-axis rotation)\n",
    "    t3 = 2.0 * (w * z + x * y)\n",
    "    t4 = 1.0 - 2.0 * (ysqr + z * z)\n",
    "    roll = math.atan2(t3, t4)\n",
    "    \n",
    "    print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))\n",
    "    \n",
    "\t# 单位转换：将弧度转换为度\n",
    "    Y = int((pitch/math.pi)*180)\n",
    "    X = int((yaw/math.pi)*180)\n",
    "    Z = int((roll/math.pi)*180)\n",
    "    \n",
    "    return 0, Y, X, Z\n",
    "\n",
    "def get_pose_estimation_in_euler_angle(landmark_shape, im_szie):\n",
    "    try:\n",
    "        ret, image_points = get_image_points_from_landmark_shape(landmark_shape)\n",
    "        if ret != 0:\n",
    "            print('get_image_points failed')\n",
    "            return -1, None, None, None\n",
    "    \n",
    "        ret, rotation_vector, translation_vector, camera_matrix, dist_coeffs = get_pose_estimation(im_szie, image_points)\n",
    "        if ret != True:\n",
    "            print('get_pose_estimation failed')\n",
    "            return -1, None, None, None\n",
    "    \n",
    "        ret, pitch, yaw, roll = get_euler_angle(rotation_vector)\n",
    "        if ret != 0:\n",
    "            print('get_euler_angle failed')\n",
    "            return -1, None, None, None\n",
    "\n",
    "        euler_angle_str = 'Y:{}, X:{}, Z:{}'.format(pitch, yaw, roll)\n",
    "        print(euler_angle_str)\n",
    "        return 0, pitch, yaw, roll\n",
    "    \n",
    "    except Exception as e:\n",
    "        print('get_pose_estimation_in_euler_angle exception:{}'.format(e))\n",
    "        return -1, None, None, None\n",
    "        \n",
    "if __name__ == '__main__':\n",
    "\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    while (cap.isOpened()):\n",
    "        start_time = time.time()\n",
    "        \n",
    "        # Read Image\n",
    "        ret, im = cap.read()\n",
    "        if ret != True:\n",
    "            print('read frame failed')\n",
    "            continue\n",
    "        size = im.shape\n",
    "        \n",
    "        if size[0] > 700:\n",
    "            h = size[0] / 3\n",
    "            w = size[1] / 3\n",
    "            im = cv2.resize( im, (int( w ), int( h )), interpolation=cv2.INTER_CUBIC )\n",
    "            size = im.shape\n",
    "     \n",
    "        ret, image_points = get_image_points(im)\n",
    "        if ret != 0:\n",
    "            print('get_image_points failed')\n",
    "            continue\n",
    "        \n",
    "        ret, rotation_vector, translation_vector, camera_matrix, dist_coeffs = get_pose_estimation(size, image_points)\n",
    "        if ret != True:\n",
    "            print('get_pose_estimation failed')\n",
    "            continue\n",
    "        used_time = time.time() - start_time\n",
    "        print(\"used_time:{} sec\".format(round(used_time, 3)))\n",
    "        \n",
    "        ret, pitch, yaw, roll = get_euler_angle(rotation_vector)\n",
    "        euler_angle_str = 'Y:{}, X:{}, Z:{}'.format(pitch, yaw, roll)\n",
    "        print(euler_angle_str)\n",
    "        \n",
    "        # Project a 3D point (0, 0, 1000.0) onto the image plane.\n",
    "        # We use this to draw a line sticking out of the nose\n",
    "         \n",
    "        (nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)\n",
    "         \n",
    "        for p in image_points:\n",
    "            cv2.circle(im, (int(p[0]), int(p[1])), 3, (0,0,255), -1)\n",
    "         \n",
    "         \n",
    "        p1 = ( int(image_points[0][0]), int(image_points[0][1]))\n",
    "        p2 = ( int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))\n",
    "         \n",
    "        cv2.line(im, p1, p2, (255,0,0), 2)\n",
    "         \n",
    "        # Display image\n",
    "        #cv2.putText( im, str(rotation_vector), (0, 100), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1 )\n",
    "        cv2.putText( im, euler_angle_str, (0, 120), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 1 )\n",
    "        # 按q退出\n",
    "        cv2.putText(frame, \"Press 'q': Quit\", (20, 500),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2)\n",
    "        # 窗口显示 show with opencv\n",
    "        cv2.imshow(\"Output\", im)\n",
    "        # if the `q` key was pressed, break from the loop\n",
    "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "            break\n",
    "    # 释放摄像头 release camera\n",
    "    cap.release()\n",
    "    # do a bit of cleanup\n",
    "    cv2.destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "pitch:-10.10757527734896, yaw:-9.880016220869354, roll:-0.39564056105204265\n",
      "pitch:-10.095086706089845, yaw:-11.326507899411984, roll:-0.9634391901375241\n",
      "pitch:-10.095086706089845, yaw:-11.326507899411984, roll:-0.9634391901375241\n",
      "pitch:-12.802446064581767, yaw:-8.064815110988713, roll:-0.9494891851228191\n",
      "pitch:-9.901723351100664, yaw:-8.927865248221183, roll:-0.9405795621895291\n",
      "pitch:-11.7970025509101, yaw:-8.296399613406734, roll:-0.889845044089612\n",
      "pitch:-10.805933834712802, yaw:-7.737920350772438, roll:-0.904833331808418\n",
      "pitch:-10.44427363726284, yaw:-7.838882107694402, roll:-0.6639065943038265\n",
      "pitch:-10.44427363726284, yaw:-7.838882107694402, roll:-0.6639065943038265\n",
      "pitch:-9.915705256880743, yaw:-8.131250699371039, roll:-1.1939399803300488\n",
      "pitch:-11.155485501923609, yaw:-6.478059536219652, roll:-0.7702457666069364\n",
      "pitch:-10.959836315417373, yaw:-6.78403629012659, roll:-0.7131500697149352\n",
      "pitch:-10.070312566516177, yaw:-7.610044233043805, roll:-0.8847356879545837\n",
      "pitch:-10.070312566516177, yaw:-7.610044233043805, roll:-0.8847356879545837\n",
      "pitch:-8.288233523399523, yaw:-7.09586953638027, roll:-1.4775969190901146\n",
      "pitch:-8.283145859348327, yaw:-6.648635479418828, roll:-2.703644394040068\n",
      "pitch:-10.21261438914622, yaw:-6.477518728908205, roll:-4.150074955575\n",
      "pitch:-7.434840930040767, yaw:-4.941406717090552, roll:-4.534048496726234\n",
      "pitch:-6.101808144755771, yaw:-4.547325824480133, roll:-4.85673002702479\n",
      "pitch:-5.474893990383474, yaw:-5.743323564221713, roll:-5.099140201529743\n",
      "pitch:-5.935556544931166, yaw:-5.067996431294465, roll:-4.996753411872619\n",
      "pitch:-5.935556544931166, yaw:-5.067996431294465, roll:-4.996753411872619\n",
      "pitch:-7.017215185742234, yaw:-5.666869106098982, roll:-4.393579363807628\n",
      "pitch:-7.9884310064769615, yaw:-6.682304289142254, roll:-4.156139380557045\n",
      "pitch:-5.8314713229858155, yaw:-4.769989965214758, roll:-4.216490729011189\n",
      "pitch:-4.813215841104321, yaw:-5.784303340013523, roll:-4.24926531895325\n",
      "pitch:-4.813215841104321, yaw:-5.784303340013523, roll:-4.24926531895325\n",
      "pitch:-7.4410389752573955, yaw:-6.1819942619000585, roll:-3.499945594139011\n",
      "pitch:-7.4410389752573955, yaw:-6.1819942619000585, roll:-3.499945594139011\n",
      "pitch:-6.5007868382106855, yaw:-7.405850006237912, roll:-3.6399722634730503\n"
     ]
    }
   ],
   "source": [
    "# 参考https://github.com/lincolnhard/head-pose-estimation\n",
    "import cv2\n",
    "import dlib\n",
    "import numpy as np\n",
    "from imutils import face_utils\n",
    "\"\"\"\n",
    "思路：\n",
    "    第一步：2D人脸关键点检测；第二步：3D人脸模型匹配；\n",
    "    第三步：求解3D点和对应2D点的转换关系；第四步：根据旋转矩阵求解欧拉角。\n",
    "\"\"\"\n",
    "\n",
    "# 加载人脸检测和姿势估计模型（dlib）\n",
    "face_landmark_path = 'D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat'\n",
    "\n",
    "\"\"\"\n",
    "只要知道世界坐标系内点的位置、像素坐标位置和相机参数就可以搞定旋转和平移矩阵(OpenCV自带函数solvePnp())\n",
    "\"\"\"\n",
    "\n",
    "# 世界坐标系(UVW)：填写3D参考点，该模型参考http://aifi.isr.uc.pt/Downloads/OpenGL/glAnthropometric3DModel.cpp\n",
    "object_pts = np.float32([[6.825897, 6.760612, 4.402142],  #33左眉左上角\n",
    "                         [1.330353, 7.122144, 6.903745],  #29左眉右角\n",
    "                         [-1.330353, 7.122144, 6.903745], #34右眉左角\n",
    "                         [-6.825897, 6.760612, 4.402142], #38右眉右上角\n",
    "                         [5.311432, 5.485328, 3.987654],  #13左眼左上角\n",
    "                         [1.789930, 5.393625, 4.413414],  #17左眼右上角\n",
    "                         [-1.789930, 5.393625, 4.413414], #25右眼左上角\n",
    "                         [-5.311432, 5.485328, 3.987654], #21右眼右上角\n",
    "                         [2.005628, 1.409845, 6.165652],  #55鼻子左上角\n",
    "                         [-2.005628, 1.409845, 6.165652], #49鼻子右上角\n",
    "                         [2.774015, -2.080775, 5.048531], #43嘴左上角\n",
    "                         [-2.774015, -2.080775, 5.048531],#39嘴右上角\n",
    "                         [0.000000, -3.116408, 6.097667], #45嘴中央下角\n",
    "                         [0.000000, -7.415691, 4.070434]])#6下巴角\n",
    "\n",
    "# 相机坐标系(XYZ)：添加相机内参\n",
    "K = [6.5308391993466671e+002, 0.0, 3.1950000000000000e+002,\n",
    "     0.0, 6.5308391993466671e+002, 2.3950000000000000e+002,\n",
    "     0.0, 0.0, 1.0]# 等价于矩阵[fx, 0, cx; 0, fy, cy; 0, 0, 1]\n",
    "# 图像中心坐标系(uv)：相机畸变参数[k1, k2, p1, p2, k3]\n",
    "D = [7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e+000]\n",
    "\n",
    "# 像素坐标系(xy)：填写凸轮的本征和畸变系数\n",
    "cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)\n",
    "dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)\n",
    "\n",
    "\n",
    "\n",
    "# 重新投影3D点的世界坐标轴以验证结果姿势\n",
    "reprojectsrc = np.float32([[10.0, 10.0, 10.0],\n",
    "                           [10.0, 10.0, -10.0],\n",
    "                           [10.0, -10.0, -10.0],\n",
    "                           [10.0, -10.0, 10.0],\n",
    "                           [-10.0, 10.0, 10.0],\n",
    "                           [-10.0, 10.0, -10.0],\n",
    "                           [-10.0, -10.0, -10.0],\n",
    "                           [-10.0, -10.0, 10.0]])\n",
    "# 绘制正方体12轴\n",
    "line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],\n",
    "              [4, 5], [5, 6], [6, 7], [7, 4],\n",
    "              [0, 4], [1, 5], [2, 6], [3, 7]]\n",
    "\n",
    "def get_head_pose(shape):\n",
    "    # 填写2D参考点，注释遵循https://ibug.doc.ic.ac.uk/resources/300-W/\n",
    "    \"\"\"\n",
    "      17左眉左上角/21左眉右角/22右眉左上角/26右眉右上角/36左眼左上角/39左眼右上角/42右眼左上角/\n",
    "      45右眼右上角/31鼻子左上角/35鼻子右上角/48左上角/54嘴右上角/57嘴中央下角/8下巴角\n",
    "    \"\"\"\n",
    "    # 像素坐标集合\n",
    "    image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],\n",
    "                            shape[39], shape[42], shape[45], shape[31], shape[35],\n",
    "                            shape[48], shape[54], shape[57], shape[8]])\n",
    "    \"\"\"\n",
    "    用solvepnp或sovlepnpRansac，输入3d点、2d点、相机内参、相机畸变，输出r、t之后\n",
    "    用projectPoints，输入3d点、相机内参、相机畸变、r、t，输出重投影2d点\n",
    "    计算原2d点和重投影2d点的距离作为重投影误差\n",
    "    \"\"\"\n",
    "    # solvePnP计算姿势——求解旋转和平移矩阵：\n",
    "    # rotation_vec表示旋转矩阵，translation_vec表示平移矩阵，cam_matrix与K矩阵对应，dist_coeffs与D矩阵对应。\n",
    "    _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)\n",
    "    # projectPoints重新投影误差\n",
    "    reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,dist_coeffs)\n",
    "\n",
    "    reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))# 以8行2列显示\n",
    "\n",
    "    # 计算欧拉角calc euler angle\n",
    "    # 参考https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#decomposeprojectionmatrix\n",
    "    rotation_mat, _ = cv2.Rodrigues(rotation_vec)#罗德里格斯公式（将旋转矩阵转换为旋转向量）\n",
    "    pose_mat = cv2.hconcat((rotation_mat, translation_vec))# 水平拼接，vconcat垂直拼接\n",
    "    # eulerAngles –可选的三元素矢量，包含三个以度为单位的欧拉旋转角度\n",
    "    _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)# 将投影矩阵分解为旋转矩阵和相机矩阵\n",
    "\n",
    "    return reprojectdst, euler_angle\n",
    "\n",
    "\n",
    "def main():\n",
    "    # return\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    if not cap.isOpened():\n",
    "        print(\"Unable to connect to camera.\")\n",
    "        return\n",
    "    # 检测人脸\n",
    "    detector = dlib.get_frontal_face_detector()\n",
    "    # 检测第一个人脸的关键点\n",
    "    predictor = dlib.shape_predictor(face_landmark_path)\n",
    "\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        if ret:\n",
    "            face_rects = detector(frame, 0)\n",
    "\n",
    "            if len(face_rects) > 0:\n",
    "                # 循环脸部位置信息，使用predictor(gray, rect)获得脸部特征位置的信息\n",
    "                shape = predictor(frame, face_rects[0])\n",
    "                # 将脸部特征信息转换为数组array的格式\n",
    "                shape = face_utils.shape_to_np(shape)\n",
    "                # 获取头部姿态\n",
    "                reprojectdst, euler_angle = get_head_pose(shape)\n",
    "                pitch = format(euler_angle[0, 0])\n",
    "                yaw = format(euler_angle[1, 0])\n",
    "                roll = format(euler_angle[2, 0])\n",
    "                print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))\n",
    "                \n",
    "                # 标出68个特征点\n",
    "                for (x, y) in shape:\n",
    "                    cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)\n",
    "                    \n",
    "                # 绘制正方体12轴\n",
    "                for start, end in line_pairs:\n",
    "                    cv2.line(frame, reprojectdst[start], reprojectdst[end], (0, 0, 255))\n",
    "                # 显示角度结果\n",
    "                cv2.putText(frame, \"X: \" + \"{:7.2f}\".format(euler_angle[0, 0]), (20, 20), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)\n",
    "                cv2.putText(frame, \"Y: \" + \"{:7.2f}\".format(euler_angle[1, 0]), (20, 50), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)\n",
    "                cv2.putText(frame, \"Z: \" + \"{:7.2f}\".format(euler_angle[2, 0]), (20, 80), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)    \n",
    "    \n",
    "            # 按q退出提示\n",
    "            cv2.putText(frame, \"Press 'q': Quit\", (20, 450),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2)\n",
    "            # 窗口显示 show with opencv\n",
    "            cv2.imshow(\"Head_Posture\", frame)\n",
    "\n",
    "            if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "                break\n",
    "    # 释放摄像头 release camera\n",
    "    cap.release()\n",
    "    # do a bit of cleanup\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading facial landmark predictor...\n",
      "pitch:-4.804883946492844, yaw:5.770244360102852, roll:7.74171155417443\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.12 \t是否眨眼：True\n",
      "pitch:-5.787747731528659, yaw:7.175109500430537, roll:7.394245394316196\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.10 \t是否眨眼：True\n",
      "pitch:-5.175370255050864, yaw:7.91738675335358, roll:7.44049987457066\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\mydownload\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:257: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "眼睛实时长宽比:0.17 \t是否眨眼：True\n",
      "pitch:-3.417337430548953, yaw:7.882616332864543, roll:7.586535310282108\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.21 \t是否眨眼：False\n",
      "pitch:-3.6201771284730215, yaw:7.340429220026476, roll:7.7019484999778784\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-2.9474014580855474, yaw:6.6989419328776725, roll:7.581566025809672\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-2.656883270625584, yaw:6.891667613620012, roll:7.770281951775834\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-2.5584633180673033, yaw:7.228725418651386, roll:7.55791260229841\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-1.6390507723713066, yaw:7.588171158524226, roll:7.99493997075737\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-2.7424520985192005, yaw:8.184204950605924, roll:7.58847447388287\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "pitch:-1.8510234679978292, yaw:7.08853268938259, roll:7.681401909936735\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "pitch:-1.8510234679978292, yaw:7.08853268938259, roll:7.681401909936735\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "pitch:-2.341761921598067, yaw:6.0713888733934125, roll:7.905154633013662\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-2.9496214470579605, yaw:6.32871600107567, roll:7.812351275714337\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "pitch:-3.3306088035860992, yaw:6.202572450676563, roll:7.370331130447683\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.36 \t是否眨眼：False\n",
      "pitch:-2.0056551028308283, yaw:5.099841131067861, roll:6.855289396256896\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-2.0056551028308283, yaw:5.099841131067861, roll:6.855289396256896\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-0.4805844446480369, yaw:5.5701729742827295, roll:6.377699157811981\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "pitch:0.8289961847497044, yaw:5.176024589087164, roll:5.702990870709301\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:2.4722617608959045, yaw:2.1843083178777047, roll:5.0009725345220195\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "pitch:2.9348891665308074, yaw:3.9770250211821185, roll:4.327518983793945\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "pitch:2.9348891665308074, yaw:3.9770250211821185, roll:4.327518983793945\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.34 \t是否眨眼：False\n",
      "pitch:3.1691248320204437, yaw:2.1541298783316485, roll:4.322988941509836\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "pitch:3.6974963634869145, yaw:4.52283068806307, roll:4.133149030790359\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
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      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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      "pitch:-6.093396963583421, yaw:3.3369402427029344, roll:3.5738450573465417\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "嘴巴实时长宽比:0.30 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.37 \t是否眨眼：False\n",
      "嘴巴实时长宽比:0.30 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.37 \t是否眨眼：False\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "pitch:-6.006311564333248, yaw:-2.314585488501808, roll:10.104361755683371\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.32 \t是否眨眼：False\n",
      "pitch:-5.941709893741865, yaw:-2.5451138281736294, roll:9.705925092073603\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-5.941709893741865, yaw:-2.5451138281736294, roll:9.705925092073603\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-6.580566453429596, yaw:-2.1554981587743622, roll:9.845758296719902\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-6.018880637385226, yaw:-1.4691689454331232, roll:9.898915942640112\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-6.018880637385226, yaw:-1.4691689454331232, roll:9.898915942640112\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-6.02136802432159, yaw:-2.686931280674132, roll:9.64759347212154\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "pitch:-6.02136802432159, yaw:-2.686931280674132, roll:9.64759347212154\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.35 \t是否眨眼：False\n",
      "pitch:-5.760225449999243, yaw:-0.8679195228031572, roll:9.86664569251054\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-5.760225449999243, yaw:-0.8679195228031572, roll:9.86664569251054\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-6.430104879478013, yaw:-1.5195246069647899, roll:9.8466024960157\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-6.430104879478013, yaw:-1.5195246069647899, roll:9.8466024960157\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-5.382665731655775, yaw:-1.6321599967270732, roll:9.637567414822575\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-5.382665731655775, yaw:-1.6321599967270732, roll:9.637567414822575\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-5.766445865754431, yaw:-1.8890775444795402, roll:9.426740173799388\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-5.766445865754431, yaw:-1.8890775444795402, roll:9.426740173799388\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.33 \t是否眨眼：False\n",
      "pitch:-5.4840329855984, yaw:-1.0440777962087215, roll:9.511151942660277\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-5.4840329855984, yaw:-1.0440777962087215, roll:9.511151942660277\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.31 \t是否眨眼：False\n",
      "pitch:-7.55755438615423, yaw:-3.382355478287317, roll:8.937405146927556\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.17 \t是否眨眼：True\n",
      "pitch:-7.55755438615423, yaw:-3.382355478287317, roll:8.937405146927556\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.17 \t是否眨眼：True\n",
      "pitch:-6.474674688898707, yaw:-1.533787842363056, roll:9.489950766256088\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-6.474674688898707, yaw:-1.533787842363056, roll:9.489950766256088\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-6.680397967823854, yaw:-1.4559222563360914, roll:9.312728972449918\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.26 \t是否眨眼：False\n",
      "pitch:-6.304012104360928, yaw:-1.1316094760421458, roll:9.128858250472572\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-6.304012104360928, yaw:-1.1316094760421458, roll:9.128858250472572\n",
      "嘴巴实时长宽比:0.43 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-6.28018734747523, yaw:-1.005186466358967, roll:9.462745743780447\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-6.28018734747523, yaw:-1.005186466358967, roll:9.462745743780447\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-6.111388781055059, yaw:-0.4975336616258818, roll:9.340983950304553\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-6.111388781055059, yaw:-0.4975336616258818, roll:9.340983950304553\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-4.410545215076453, yaw:-0.7362467342792053, roll:9.229142190003053\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-4.410545215076453, yaw:-0.7362467342792053, roll:9.229142190003053\n",
      "嘴巴实时长宽比:0.49 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-3.0419094543077816, yaw:-1.1942738934067698, roll:9.207425668322918\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-5.7036138143745925, yaw:1.63582528125027, roll:8.982710553785711\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-5.210264459818207, yaw:1.4895739914888613, roll:8.77344521864328\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-5.210264459818207, yaw:1.4895739914888613, roll:8.77344521864328\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-6.344730130286309, yaw:1.3568923299524505, roll:9.023177629035214\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-5.181899612574912, yaw:2.2532862183774123, roll:9.06660036993171\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-5.089009970112675, yaw:0.7319124190237006, roll:8.906444859584408\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-5.348188770645712, yaw:1.347929165618272, roll:8.807804872685566\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.30 \t是否眨眼：False\n",
      "pitch:-4.593648326146274, yaw:0.8989646023026882, roll:8.952487163456\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.29 \t是否眨眼：False\n",
      "pitch:-4.760917727411323, yaw:2.3636643466711154, roll:9.169369704020014\n",
      "嘴巴实时长宽比:0.44 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.28 \t是否眨眼：False\n",
      "pitch:-5.234597225442251, yaw:1.0227156901300343, roll:8.613555146867354\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-5.234597225442251, yaw:1.0227156901300343, roll:8.613555146867354\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-4.871959448892153, yaw:1.3452094201222466, roll:8.785589077789822\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "pitch:-6.139072421376027, yaw:1.1988253378809668, roll:8.953427141459096\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.24 \t是否眨眼：False\n",
      "pitch:-6.471171023696999, yaw:1.0453442551341185, roll:8.95337725968589\n",
      "嘴巴实时长宽比:0.46 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-5.413557778813256, yaw:0.5739977783455212, roll:8.979426948655624\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "pitch:-5.413557778813256, yaw:0.5739977783455212, roll:8.979426948655624\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "pitch:-5.724821439267839, yaw:1.3297020548287355, roll:9.08813150107879\n",
      "嘴巴实时长宽比:0.48 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.24 \t是否眨眼：False\n",
      "pitch:-5.277625923303347, yaw:1.4492772571016523, roll:9.245460413049285\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-5.631078987772558, yaw:2.8634428252591864, roll:9.428837687355273\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-5.631078987772558, yaw:2.8634428252591864, roll:9.428837687355273\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-6.405233860968585, yaw:0.9754083841432815, roll:9.384573524277027\n",
      "嘴巴实时长宽比:0.47 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.25 \t是否眨眼：False\n",
      "pitch:-6.818250736860751, yaw:1.1624419659228802, roll:9.155440097250603\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.23 \t是否眨眼：False\n",
      "pitch:-7.0921397245058175, yaw:0.3175194561894617, roll:8.946686803371612\n",
      "嘴巴实时长宽比:0.45 \t是否张嘴：False\n",
      "眼睛实时长宽比:0.16 \t是否眨眼：True\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# import the necessary packages\n",
    "from scipy.spatial import distance as dist\n",
    "from imutils.video import FileVideoStream\n",
    "from imutils.video import VideoStream\n",
    "from imutils import face_utils\n",
    "import numpy as np # 数据处理的库 numpy\n",
    "import argparse\n",
    "import imutils\n",
    "import time\n",
    "import dlib\n",
    "import cv2\n",
    "import math\n",
    "import time\n",
    "from threading import Thread,Event\n",
    " \n",
    "# 世界坐标系(UVW)：填写3D参考点，该模型参考http://aifi.isr.uc.pt/Downloads/OpenGL/glAnthropometric3DModel.cpp\n",
    "object_pts = np.float32([[6.825897, 6.760612, 4.402142],  #33左眉左上角\n",
    "                         [1.330353, 7.122144, 6.903745],  #29左眉右角\n",
    "                         [-1.330353, 7.122144, 6.903745], #34右眉左角\n",
    "                         [-6.825897, 6.760612, 4.402142], #38右眉右上角\n",
    "                         [5.311432, 5.485328, 3.987654],  #13左眼左上角\n",
    "                         [1.789930, 5.393625, 4.413414],  #17左眼右上角\n",
    "                         [-1.789930, 5.393625, 4.413414], #25右眼左上角\n",
    "                         [-5.311432, 5.485328, 3.987654], #21右眼右上角\n",
    "                         [2.005628, 1.409845, 6.165652],  #55鼻子左上角\n",
    "                         [-2.005628, 1.409845, 6.165652], #49鼻子右上角\n",
    "                         [2.774015, -2.080775, 5.048531], #43嘴左上角\n",
    "                         [-2.774015, -2.080775, 5.048531],#39嘴右上角\n",
    "                         [0.000000, -3.116408, 6.097667], #45嘴中央下角\n",
    "                         [0.000000, -7.415691, 4.070434]])#6下巴角\n",
    "\n",
    "# 相机坐标系(XYZ)：添加相机内参\n",
    "K = [6.5308391993466671e+002, 0.0, 3.1950000000000000e+002,\n",
    "     0.0, 6.5308391993466671e+002, 2.3950000000000000e+002,\n",
    "     0.0, 0.0, 1.0]# 等价于矩阵[fx, 0, cx; 0, fy, cy; 0, 0, 1]\n",
    "# 图像中心坐标系(uv)：相机畸变参数[k1, k2, p1, p2, k3]\n",
    "D = [7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e+000]\n",
    "\n",
    "# 像素坐标系(xy)：填写凸轮的本征和畸变系数\n",
    "cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)\n",
    "dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)\n",
    "\n",
    "\n",
    "\n",
    "# 重新投影3D点的世界坐标轴以验证结果姿势\n",
    "reprojectsrc = np.float32([[10.0, 10.0, 10.0],\n",
    "                           [10.0, 10.0, -10.0],\n",
    "                           [10.0, -10.0, -10.0],\n",
    "                           [10.0, -10.0, 10.0],\n",
    "                           [-10.0, 10.0, 10.0],\n",
    "                           [-10.0, 10.0, -10.0],\n",
    "                           [-10.0, -10.0, -10.0],\n",
    "                           [-10.0, -10.0, 10.0]])\n",
    "# 绘制正方体12轴\n",
    "line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],\n",
    "              [4, 5], [5, 6], [6, 7], [7, 4],\n",
    "              [0, 4], [1, 5], [2, 6], [3, 7]]\n",
    "\n",
    "def get_head_pose(shape):# 头部姿态估计\n",
    "    # （像素坐标集合）填写2D参考点，注释遵循https://ibug.doc.ic.ac.uk/resources/300-W/\n",
    "    # 17左眉左上角/21左眉右角/22右眉左上角/26右眉右上角/36左眼左上角/39左眼右上角/42右眼左上角/\n",
    "    # 45右眼右上角/31鼻子左上角/35鼻子右上角/48左上角/54嘴右上角/57嘴中央下角/8下巴角\n",
    "    image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],\n",
    "                            shape[39], shape[42], shape[45], shape[31], shape[35],\n",
    "                            shape[48], shape[54], shape[57], shape[8]])\n",
    "    # solvePnP计算姿势——求解旋转和平移矩阵：\n",
    "    # rotation_vec表示旋转矩阵，translation_vec表示平移矩阵，cam_matrix与K矩阵对应，dist_coeffs与D矩阵对应。\n",
    "    _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)\n",
    "    # projectPoints重新投影误差：原2d点和重投影2d点的距离（输入3d点、相机内参、相机畸变、r、t，输出重投影2d点）\n",
    "    reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,dist_coeffs)\n",
    "    reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))# 以8行2列显示\n",
    "\n",
    "    # 计算欧拉角calc euler angle\n",
    "    # 参考https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#decomposeprojectionmatrix\n",
    "    rotation_mat, _ = cv2.Rodrigues(rotation_vec)#罗德里格斯公式（将旋转矩阵转换为旋转向量）\n",
    "    pose_mat = cv2.hconcat((rotation_mat, translation_vec))# 水平拼接，vconcat垂直拼接\n",
    "    # decomposeProjectionMatrix将投影矩阵分解为旋转矩阵和相机矩阵\n",
    "    _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)\n",
    "    \n",
    "    pitch, yaw, roll = [math.radians(_) for _ in euler_angle]\n",
    " \n",
    " \n",
    "    pitch = math.degrees(math.asin(math.sin(pitch)))\n",
    "    roll = -math.degrees(math.asin(math.sin(roll)))\n",
    "    yaw = math.degrees(math.asin(math.sin(yaw)))\n",
    "    print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))\n",
    "\n",
    "    return reprojectdst, euler_angle# 投影误差，欧拉角\n",
    "\n",
    "def eye_aspect_ratio(eye):\n",
    "    # 垂直眼标志（X，Y）坐标\n",
    "    A = dist.euclidean(eye[1], eye[5])# 计算两个集合之间的欧式距离\n",
    "    B = dist.euclidean(eye[2], eye[4])\n",
    "    # 计算水平之间的欧几里得距离\n",
    "    # 水平眼标志（X，Y）坐标\n",
    "    C = dist.euclidean(eye[0], eye[3])\n",
    "    # 眼睛长宽比的计算\n",
    "    ear = (A + B) / (2.0 * C)\n",
    "    # 返回眼睛的长宽比\n",
    "    return ear\n",
    " \n",
    "def mouth_aspect_ratio(mouth):# 嘴部\n",
    "    A = np.linalg.norm(mouth[2] - mouth[9])  # 51, 59\n",
    "    B = np.linalg.norm(mouth[4] - mouth[7])  # 53, 57\n",
    "    C = np.linalg.norm(mouth[0] - mouth[6])  # 49, 55\n",
    "    mar = (A + B) / (2.0 * C)\n",
    "    return mar\n",
    "\n",
    "# 定义常数\n",
    "# 眼睛长宽比\n",
    "# 闪烁阈值\n",
    "EYE_AR_THRESH = 0.2\n",
    "EYE_AR_CONSEC_FRAMES = 3\n",
    "# 打哈欠长宽比\n",
    "# 闪烁阈值\n",
    "MAR_THRESH = 0.5\n",
    "MOUTH_AR_CONSEC_FRAMES = 3\n",
    "# 瞌睡点头\n",
    "HAR_THRESH = 0.3\n",
    "NOD_AR_CONSEC_FRAMES = 3\n",
    "# 初始化帧计数器和眨眼总数\n",
    "COUNTER = 0\n",
    "TOTAL = 0\n",
    "# 初始化帧计数器和打哈欠总数\n",
    "mCOUNTER = 0\n",
    "mTOTAL = 0\n",
    "# 初始化帧计数器和点头总数\n",
    "hCOUNTER = 0\n",
    "hTOTAL = 0\n",
    "\n",
    "# 初始化DLIB的人脸检测器（HOG），然后创建面部标志物预测\n",
    "print(\"[INFO] loading facial landmark predictor...\")\n",
    "# 第一步：使用dlib.get_frontal_face_detector() 获得脸部位置检测器\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "# 第二步：使用dlib.shape_predictor获得脸部特征位置检测器\n",
    "predictor = dlib.shape_predictor('D:/myworkspace/JupyterNotebook/fatigue_detecting/model/shape_predictor_68_face_landmarks.dat')\n",
    " \n",
    "# 第三步：分别获取左右眼面部标志的索引\n",
    "(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"left_eye\"]\n",
    "(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"right_eye\"]\n",
    "(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"mouth\"]\n",
    "\n",
    "# 第四步：打开cv2 本地摄像头\n",
    "cap = cv2.VideoCapture(0)\n",
    " \n",
    "# 从视频流循环帧\n",
    "while True:\n",
    "    # 第五步：进行循环，读取图片，并对图片做维度扩大，并进灰度化\n",
    "    ret, frame = cap.read()\n",
    "    frame = imutils.resize(frame, width=720)\n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "    # 第六步：使用detector(gray, 0) 进行脸部位置检测\n",
    "    rects = detector(gray, 0)\n",
    "    \n",
    "    # 第七步：循环脸部位置信息，使用predictor(gray, rect)获得脸部特征位置的信息\n",
    "    for rect in rects:\n",
    "        shape = predictor(gray, rect)\n",
    "        \n",
    "        # 第八步：将脸部特征信息转换为数组array的格式\n",
    "        shape = face_utils.shape_to_np(shape)\n",
    "        \n",
    "        # 第九步：提取左眼和右眼坐标\n",
    "        leftEye = shape[lStart:lEnd]\n",
    "        rightEye = shape[rStart:rEnd]\n",
    "        # 嘴巴坐标\n",
    "        mouth = shape[mStart:mEnd]        \n",
    "        \n",
    "        # 第十步：构造函数计算左右眼的EAR值，使用平均值作为最终的EAR\n",
    "        leftEAR = eye_aspect_ratio(leftEye)\n",
    "        rightEAR = eye_aspect_ratio(rightEye)\n",
    "        ear = (leftEAR + rightEAR) / 2.0\n",
    "        # 打哈欠\n",
    "        mar = mouth_aspect_ratio(mouth)\n",
    " \n",
    "        # 第十一步：使用cv2.convexHull获得凸包位置，使用drawContours画出轮廓位置进行画图操作\n",
    "        leftEyeHull = cv2.convexHull(leftEye)\n",
    "        rightEyeHull = cv2.convexHull(rightEye)\n",
    "        cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\n",
    "        cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\n",
    "        mouthHull = cv2.convexHull(mouth)\n",
    "        cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1)\n",
    " \n",
    "        # 第十二步：进行画图操作，用矩形框标注人脸\n",
    "        left = rect.left()\n",
    "        top = rect.top()\n",
    "        right = rect.right()\n",
    "        bottom = rect.bottom()\n",
    "        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 1)    \n",
    " \n",
    "        '''\n",
    "            分别计算左眼和右眼的评分求平均作为最终的评分，如果小于阈值，则加1，如果连续3次都小于阈值，则表示进行了一次眨眼活动\n",
    "        '''\n",
    "        # 第十三步：循环，满足条件的，眨眼次数+1\n",
    "        if ear < EYE_AR_THRESH:# 眼睛长宽比：0.2\n",
    "            COUNTER += 1\n",
    "           \n",
    "        else:\n",
    "            # 如果连续3次都小于阈值，则表示进行了一次眨眼活动\n",
    "            if COUNTER >= EYE_AR_CONSEC_FRAMES:# 阈值：3\n",
    "                TOTAL += 1\n",
    "            # 重置眼帧计数器\n",
    "            COUNTER = 0\n",
    "            \n",
    "        # 第十四步：进行画图操作，同时使用cv2.putText将眨眼次数进行显示\n",
    "        cv2.putText(frame, \"Faces: {}\".format(len(rects)), (10, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)     \n",
    "        cv2.putText(frame, \"COUNTER: {}\".format(COUNTER), (150, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) \n",
    "        cv2.putText(frame, \"EAR: {:.2f}\".format(ear), (300, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.putText(frame, \"Blinks: {}\".format(TOTAL), (450, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,0), 2)\n",
    "        \n",
    "        '''\n",
    "            计算张嘴评分，如果小于阈值，则加1，如果连续3次都小于阈值，则表示打了一次哈欠，同一次哈欠大约在3帧\n",
    "        '''\n",
    "        # 同理，判断是否打哈欠    \n",
    "        if mar > MAR_THRESH:# 张嘴阈值0.5\n",
    "            mCOUNTER += 1\n",
    "            cv2.putText(frame, \"Yawning!\", (10, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        else:\n",
    "            # 如果连续3次都小于阈值，则表示打了一次哈欠\n",
    "            if mCOUNTER >= MOUTH_AR_CONSEC_FRAMES:# 阈值：3\n",
    "                mTOTAL += 1\n",
    "            # 重置嘴帧计数器\n",
    "            mCOUNTER = 0\n",
    "        cv2.putText(frame, \"COUNTER: {}\".format(mCOUNTER), (150, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) \n",
    "        cv2.putText(frame, \"MAR: {:.2f}\".format(mar), (300, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
    "        cv2.putText(frame, \"Yawning: {}\".format(mTOTAL), (450, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,0), 2)\n",
    "        \"\"\"\n",
    "        瞌睡点头\n",
    "        \"\"\"\n",
    "        # 第十五步：获取头部姿态\n",
    "        reprojectdst, euler_angle = get_head_pose(shape)\n",
    "        \n",
    "        har = euler_angle[0, 0]# 取pitch旋转角度\n",
    "        if har > HAR_THRESH:# 点头阈值0.3\n",
    "            hCOUNTER += 1\n",
    "        else:\n",
    "            # 如果连续3次都小于阈值，则表示瞌睡点头一次\n",
    "            if hCOUNTER >= NOD_AR_CONSEC_FRAMES:# 阈值：3\n",
    "                hTOTAL += 1\n",
    "            # 重置点头帧计数器\n",
    "            hCOUNTER = 0\n",
    "        \n",
    "        # 绘制正方体12轴\n",
    "        for start, end in line_pairs:\n",
    "            cv2.line(frame, reprojectdst[start], reprojectdst[end], (0, 0, 255))\n",
    "        # 显示角度结果\n",
    "        cv2.putText(frame, \"X: \" + \"{:7.2f}\".format(euler_angle[0, 0]), (10, 90), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 255, 0), thickness=2)# GREEN\n",
    "        cv2.putText(frame, \"Y: \" + \"{:7.2f}\".format(euler_angle[1, 0]), (150, 90), cv2.FONT_HERSHEY_SIMPLEX,0.75, (255, 0, 0), thickness=2)# BLUE\n",
    "        cv2.putText(frame, \"Z: \" + \"{:7.2f}\".format(euler_angle[2, 0]), (300, 90), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)# RED    \n",
    "        cv2.putText(frame, \"Nod: {}\".format(hTOTAL), (450, 90),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,0), 2)\n",
    "        \n",
    "            \n",
    "        # 第十六步：进行画图操作，68个特征点标识\n",
    "        for (x, y) in shape:\n",
    "            cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)\n",
    "\n",
    "    print('嘴巴实时长宽比:{:.2f} '.format(mar)+\"\\t是否张嘴：\"+str([False,True][mar > MAR_THRESH]))\n",
    "    print('眼睛实时长宽比:{:.2f} '.format(ear)+\"\\t是否眨眼：\"+str([False,True][COUNTER>=1]))\n",
    "    \n",
    "    # 确定疲劳提示:眨眼50次，打哈欠15次，瞌睡点头15次\n",
    "    if TOTAL >= 50 or mTOTAL>=15 or hTOTAL>=15:\n",
    "        cv2.putText(frame, \"SLEEP!!!\", (100, 200),cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 3)\n",
    "        \n",
    "    # 按q退出\n",
    "    cv2.putText(frame, \"Press 'q': Quit\", (20, 500),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2)\n",
    "    # 窗口显示 show with opencv\n",
    "    cv2.imshow(\"Frame\", frame)\n",
    "    \n",
    "    # if the `q` key was pressed, break from the loop\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "        \n",
    "# 释放摄像头 release camera\n",
    "cap.release()\n",
    "# do a bit of cleanup\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.036243467926484\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.array([5,3,0])\n",
    "y=np.array([4,4,0])\n",
    "# 两个向量\n",
    "Lx=np.sqrt(x.dot(x))\n",
    "Ly=np.sqrt(y.dot(y))\n",
    "#相当于勾股定理，求得斜线的长度\n",
    "cos_angle=x.dot(y)/(Lx*Ly)#求得cos_sita的值再反过来计算，绝对长度乘以cos角度为矢量长度\n",
    "angle=np.arccos(cos_angle)\n",
    "angle2=angle*360/2/np.pi#变为角度\n",
    "print(angle2)\n",
    "#x.dot(y) =  y=∑(ai*bi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([0,1,0])\n",
    "b = np.array([0,0,0])\n",
    "c = np.array([1,0,0])\n",
    "# 两个向量\n",
    "Lba = np.sqrt((a-b).dot(a-b))\n",
    "Lbc = np.sqrt((c-b).dot(c-b))\n",
    "cos_angle = (a-b).dot(c-b)/(Lba*Lbc)#相当于勾股定理，求得斜线的长度\n",
    "angle = np.arccos(cos_angle)#求得cos_sita的值再反过来计算，绝对长度乘以cos角度为矢量长度\n",
    "angle2=angle*360/2/np.pi#变为角度\n",
    "print(angle2)"
   ]
  }
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